Pose-Guided Camera Selection for Real-Time Item Identification
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Solution Overview
Problem
Conventional image processing systems for identifying and tracking multiple items are computationally intensive and time-consuming, making them incompatible with real-time applications, especially when multiple items need to be identified and tracked simultaneously.
Innovation Solution
A system utilizing a combination of cameras and 3D sensors to capture and process images of items on a platform, selecting optimal cameras based on item pose, and employing machine learning to identify and assign items to users without manual scanning, thereby reducing the number of images processed and improving system throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing techniques are used to identify multiple items by comparing features against every item in a database, then item identification can be achieved, but the process becomes computationally intensive and time-consuming
Solution Approach 1:
The patent segments the item identification process into distinct phases: first capturing depth images to determine object poses, then using pose information to select relevant cameras, and finally processing only those selected camera images for feature extraction and matching. This segmentation avoids the computationally intensive approach of comparing all item features against every database entry, thereby improving identification speed while maintaining accuracy.
Solution Approach 2:
The system performs preliminary actions by capturing depth images and determining object poses before the actual item identification process. This preliminary step provides crucial spatial information that guides subsequent camera selection and image processing, enabling the system to focus computational resources on relevant items and views rather than processing all possible combinations.
2Reliability
If all cameras on the imaging device are used to capture images of items, then comprehensive item coverage is achieved, but the number of images to be processed increases significantly
Solution Approach 1:
The patent applies local quality by using depth image information and object pose data to determine which specific cameras provide the best views of each item. Instead of uniformly processing images from all cameras, the system selectively processes only those camera images that capture items from optimal angles, thereby reducing processing complexity while maintaining detection completeness.
Solution Approach 2:
The system dynamically selects cameras based on real-time depth image analysis and object pose determination. The camera selection is not fixed but adapts to the spatial configuration of items on the platform, enabling the system to optimize processing complexity based on the actual scene while ensuring all items are captured by at least one selected camera.
Data Source
AI summary
A device configured to detect a triggering event at a platform and to capture a depth image of items on the platform using a three-dimensional (3D) sensor. The device is further configured to determine an object pose for each item on the platform and to identify one or more cameras from among a plurality of cameras based on the object pose for each item on the platform. The device is further configured to capture one or more images of the items on the platform using the identified cameras and to identify items within the one or more images. The device is further configured to identify a user associated with the identified items on the platform, to identify an account that is associated with the user, and to associate the identified items with the account of the user.